Abstract Reports on the time-series properties and predictive ability of variables in funds-flow statement. Descriptive evidence that documents the statistical patterns of the cash flow and working capital series for a sample of firms; Measurement of the predictive ability of variables in a funds-flow statement; Security return effects.

[An accurate description of the process that generates measures of funds flow (cash flow and working capital from operations) has potential importance in a variety of decision contexts. In particular, the issue of cash flow prediction has been central to standard setters. According to Statement of Financial Accounting Concepts No. 1 (FASB 1978, par. 37) "financial reporting should provide information to help investors, creditors, and others assess the amount, timing, and uncertainty of prospective net cash inflows to the related enterprise." Our first objective in this study is to provide descriptive evidence that documents the statistical patterns (e.g., seasonality, autocorrelation) of the cash flow and working capital series for a sample of firms. Although the accounting literature is replete with evidence on the time-series properties of earnings numbers, much less systematic evidence on such properties for cash flow and working capital series is presently available. Our results suggest that the time-series behavior of the cash flow series is in marked contrast to the models typically employed for accounting net income. We also provide evidence that the working capital series behaves similarly to net income. Funds flow is important in the investigation of security price effects, and the association of various measures of funds flow with security returns has been studied by Bernard and Stober (1989), Bowen et al. (1987), Schaefer and Kennelley (1986), and Wilson (1987), among others. These studies have employed cross-sectional expectation models for funds flow measures that restrict coefficients to be the same across firms. We assess the effect of such restrictions on the predictive ability of these models by comparing them with univariate time-series models that permit firm-specific estimation of coefficients. Accordingly, our second objective is to assess the accuracy of forecasts of funds flow variables generated by univariate time-series models versus those obtained from the multivariate cross-sectional models used in prior research (see, e.g., Bernard and Stober 1989; Wilson 1986, 1987). This study provides new evidence on the time-series properties of cash flow and working capital series. The empirical results indicate that the statistical patterns of the cash flow series stand in marked contrast to the well-documented characteristics of quarterly earnings data. Cash flow series are modeled parsimoniously by purely seasonal time-series models. Specifically, we provide descriptive and predictive evidence supportive of the (000)� (100) seasonal Autoregressive Integrated Moving Average (ARIMA) model as a candidate model for predicting cash flow. This model outperforms the multivariate cross-sectional models used in prior research in out-of-sample predictive ability tests. We also present evidence that working capital from operations exhibits time-series behavior virtually identical to that of accounting earnings. This results in identification of ARIMA expectation models quite similar to those popularized for quarterly earnings. Such univariate ARIMA models for working capital dominated cross-sectional regression models in tests of predictive ability.]